The primary goal of many works that study deep generative models is to generate realistic images. It is well-known that shortrun sampling with an EBM is an effective method for image generation, but synthesis results still lag behind GANs [1]}, score models, and diffusion models [2]}. Other directions of EBM research include learning valid densities [3]}, combining discriminative and generative learning via the Joint Energy Model [4]} and relatives, and using an EBM for defense against adversarial attacks [5]}. In this work, we focus on unconditional learning where EBMs are trained exclusively on unlabeled images. We explore the tasks of image synthesis, adversarial defense, and density modeling to examine a breadth of capabilities for the unconditional EBM. Image synthesis is a shared goal across all generative models, while defense and density modeling are tasks that are especially suitable for EBMs. See Appendices REF  and REF  for a thorough comparison of EBM and other generative models from the perspective of synthesis, defense, and density estimation.
